def generate_reference_data_for_pcquick_filter_tests():
orig_long = np.arange(0., 359.9, 2.5)
test_olr = olr.load_noaa_interpolated_olr(olr_data_filename)
lat = np.array([0])
test_olr_part = olr.interpolate_spatial_grid(test_olr, lat, orig_long)
olrdata_filtered = qfilter.filter_olr_for_mjo_pc_calculation_1d_spectral_smoothing(
test_olr_part)
filename = Path(
str(reference_file_filterOLRForMJO_PCQuick_Calculation_lat0) +
".newcalc")
olrdata_filtered.save_to_npzfile(filename)
lat = np.array([5])
test_olr_part = olr.interpolate_spatial_grid(test_olr, lat, orig_long)
olrdata_filtered = qfilter.filter_olr_for_mjo_pc_calculation_1d_spectral_smoothing(
test_olr_part)
filename = Path(
str(reference_file_filterOLRForMJO_PCQuick_Calculation_lat5) +
".newcalc")
olrdata_filtered.save_to_npzfile(filename)
lat = np.array([-10])
test_olr_part = olr.interpolate_spatial_grid(test_olr, lat, orig_long)
olrdata_filtered = qfilter.filter_olr_for_mjo_pc_calculation_1d_spectral_smoothing(
test_olr_part)
filename = Path(
str(reference_file_filterOLRForMJO_PCQuick_Calculation_latmin10) +
".newcalc")
olrdata_filtered.save_to_npzfile(filename)
def test_mjoindices_reference_validation_filterOLRForMJO_PC_Calculation():
errors = []
orig_long = np.arange(0., 359.9, 2.5)
test_olr = olr.load_noaa_interpolated_olr(olr_data_filename)
lat = np.array([0])
test_olr_part = olr.interpolate_spatial_grid(test_olr, lat, orig_long)
target = wkfilter.filter_olr_for_mjo_pc_calculation(test_olr_part)
control = olr.restore_from_npzfile(reference_file_filterOLRForMJO_PC_Calculation_lat0)
if not target.close(control):
errors.append("Filtered OLR for latitude 0 not identical")
lat = np.array([5])
test_olr_part = olr.interpolate_spatial_grid(test_olr, lat, orig_long)
target = wkfilter.filter_olr_for_mjo_pc_calculation(test_olr_part)
control = olr.restore_from_npzfile(reference_file_filterOLRForMJO_PC_Calculation_lat5)
if not target.close(control):
errors.append("Filtered OLR for latitude 5 not identical")
lat = np.array([-10])
test_olr_part = olr.interpolate_spatial_grid(test_olr, lat, orig_long)
target = wkfilter.filter_olr_for_mjo_pc_calculation(test_olr_part)
control = olr.restore_from_npzfile(reference_file_filterOLRForMJO_PC_Calculation_latmin10)
if not target.close(control):
errors.append("Filtered OLR for latitude -10 not identical")
assert not errors, "errors occurred:\n{}".format("\n".join(errors))
def test_completeOMIReproduction_coarsegrid(tmp_path):
errors = []
# Calculate EOFs
raw_olr = olr.load_noaa_interpolated_olr(olr_data_filename)
shorter_olr = olr.restrict_time_coverage(raw_olr,
np.datetime64('1979-01-01'),
np.datetime64('2012-12-31'))
coarse_lat = np.arange(-20., 20.1, 8.0)
coarse_long = np.arange(0., 359.9, 20.0)
interpolated_olr = olr.interpolate_spatial_grid(shorter_olr, coarse_lat,
coarse_long)
eofs = omi.calc_eofs_from_olr(interpolated_olr,
sign_doy1reference=True,
interpolate_eofs=True,
strict_leap_year_treatment=True)
eofs.save_all_eofs_to_npzfile(
tmp_path / "test_completeOMIReproduction_coarsegrid_EOFs.npz")
# Validate EOFs against mjoindices own reference (results should be equal)
mjoindices_reference_eofs = eof.restore_all_eofs_from_npzfile(
mjoindices_reference_eofs_filename_coarsegrid)
for idx, target_eof in enumerate(eofs.eof_list):
if not mjoindices_reference_eofs.eof_list[idx].close(target_eof):
errors.append(
"mjoindices-reference-validation: EOF data at index %i is incorrect"
% idx)
# Calculate PCs
raw_olr = olr.load_noaa_interpolated_olr(olr_data_filename)
pcs = omi.calculate_pcs_from_olr(raw_olr,
eofs,
np.datetime64("1979-01-01"),
np.datetime64("2018-08-28"),
use_quick_temporal_filter=False)
pc_temp_file = tmp_path / "test_completeOMIReproduction_coarsegrid_PCs.txt"
pcs.save_pcs_to_txt_file(pc_temp_file)
# Validate PCs against mjoindices own reference (results should be equal)
# Reload pcs instead of using the calculated ones, because the saving routine has truncated some decimals of the
# reference values. So do the same with the testing target pcs.
pcs = pc.load_pcs_from_txt_file(pc_temp_file)
mjoindices_reference_pcs = pc.load_pcs_from_txt_file(
mjoindices_reference_pcs_filename_coarsegrid)
if not np.all(mjoindices_reference_pcs.time == pcs.time):
errors.append(
"mjoindices-reference-validation: Dates of PCs do not match.")
if not np.allclose(mjoindices_reference_pcs.pc1, pcs.pc1):
errors.append(
"mjoindices-reference-validation: PC1 values do not match.")
if not np.allclose(mjoindices_reference_pcs.pc2, pcs.pc2):
errors.append(
"mjoindices-reference-validation: PC2 values do not match.")
assert not errors, "errors occurred:\n{}".format("\n".join(errors))
def calculate_pcs_from_olr(olrdata: olr.OLRData,
eofdata: eof.EOFDataForAllDOYs,
period_start: np.datetime64,
period_end: np.datetime64,
use_quick_temporal_filter=False) -> pc.PCData:
"""
This major function computes PCs according to the OMI algorithm based on given OLR data and previously calculated
EOFs.
:param olrdata: The OLR dataset. The spatial grid must fit to that of the EOFs
:param eofdata: The previously calculated DOY-dependent EOFs.
:param period_start: the beginning of the period, for which the PCs should be calculated.
:param period_end: the ending of the period, for which the PCs should be calculated.
:param use_quick_temporal_filter: There are two implementations of the temporal filtering: First, the original
Wheeler-Kiladis-Filter, which is closer to the original implementation while being slower (because it is based
on a 2-dim FFT) or a 1-dim FFT Filter. Setting this parameter to True uses the quicker 1-dim implementation. The
results are quite similar.
:return: The PC time series.
"""
resticted_olr_data = olr.restrict_time_coverage(olrdata, period_start,
period_end)
resampled_olr_data = olr.interpolate_spatial_grid(resticted_olr_data,
eofdata.lat,
eofdata.long)
if use_quick_temporal_filter:
filtered_olr_data = qfilter.filter_olr_for_mjo_pc_calculation_1d_spectral_smoothing(
resampled_olr_data)
else:
filtered_olr_data = wkfilter.filter_olr_for_mjo_pc_calculation(
resampled_olr_data)
raw_pcs = regress_3dim_data_onto_eofs(filtered_olr_data, eofdata)
normalization_factor = 1 / np.std(raw_pcs.pc1)
pc1 = np.multiply(raw_pcs.pc1, normalization_factor)
pc2 = np.multiply(raw_pcs.pc2, normalization_factor)
return pc.PCData(raw_pcs.time, pc1, pc2)
'')) / "example_data" / "omi_recalc_example_plots_coarsegrid"
# ############## Calculation of the EOFs ###################
if not fig_dir.exists():
fig_dir.mkdir(parents=True, exist_ok=False)
# Load the OLR data.
# This is the first place to insert your own OLR data, if you want to compute OMI for a different dataset.
raw_olr = olr.load_noaa_interpolated_olr(olr_data_filename)
# Restrict dataset to the original length for the EOF calculation (Kiladis, 2014).
shorter_olr = olr.restrict_time_coverage(raw_olr, np.datetime64('1979-01-01'),
np.datetime64('2012-12-31'))
# This is the line, where the spatial grid is changed.
interpolated_olr = olr.interpolate_spatial_grid(shorter_olr, coarse_lat,
coarse_long)
# Diagnosis plot of the loaded OLR data.
fig = olr.plot_olr_map_for_date(interpolated_olr, np.datetime64("2010-01-01"))
fig.show()
fig.savefig(fig_dir / "OLR_map.png")
# Calculate the eofs. In the postprocessing, the signs of the EOFs are adjusted and the EOF in a period
# around DOY 300 are replaced by an interpolation see Kiladis, 2014).
# The switch strict_leap_year_treatment has major implications only for the EOFs calculated for DOY 366 and causes only
# minor differences for the other DOYs. While the results for setting strict_leap_year_treatment=False are closer to the
# original values, the calculation strict_leap_year_treatment=True is somewhat more stringently implemented using
# built-in datetime functionality.
# See documentation of mjoindices.tools.find_doy_ranges_in_dates() for details.
eofs = omi.calc_eofs_from_olr(interpolated_olr,
sign_doy1reference=True,